I'm an entrepreneur, adviser, and investor. From 2009 - 2011, I was a General Partner at Mohr Davidow Ventures, a Sand Hill Road venture capital firm with $2B under management.
I've founded five tech businesses, with acquisitions by HP, EMC, and Keynote Systems. I'm co-founder of Speechpad, the leader in crowd-sourced audio and video transcription and Onepo.st, a site that makes reposting social media content easy. I advise a number of companies including SendHub, BandPage, and LiveMinutes.
I'm the author of Why Startups Fail: And How Yours Can Succeed. I also blog actively at vcdave.com.
When I'm not working with tech startups, I enjoy triathlons, rock climbing, and playing the violin.

Some people claim Big Data is pure marketing hype. A bunch of companies looking for a way to promote their products. And to some extent that’s true of every trend in tech: social, mobile, cloud, SaaS. At one point or another they were all referred to as hype, as marketing vehicles. Some of them still are.

But the real challenge for Big Data is that it gets really technical, really fast. Don’t get me wrong. I enjoy technical. I taught myself how to develop software at age 12, worked at Microsoft at age 16, and spent a few years doing Developer Evangelism when Windows was still a relative unknown.

When it comes to Big Data, before you know it, we’re talking petabytes and exabytes, batch versus real time, Hadoop and MapReduce, machine sensors and human data. And the list goes on.

So now when I talk with people about Big Data I ask for concrete examples.

One company lost more than $100M in three days when financial traders acted on changing prices in commodities markets faster than the company could. If companies could keep pace with the real world in processing their data, they could save a ton of money.

In another example, two people have the same kind of car, are about the same age, and drive roughly the same route to and from work. There’s just one difference — they’re driving in opposite directions. Insurance companies are using data like this to deliver individualized rates.

And in case you’re wondering what “machine data” is (it’s OK if you’re not), it’s not some scene out of The Matrix.

Machine data refers to data generated by computers (and computer software), mobile phones, airplanes, and the like about their status, location, and other information. Simply put, it’s a lot of data, it all needs to be stored somewhere, and it requires some sophisticated software to glean any insight from it.

But this isn’t a post about what Big Data is. For that you can read The 3 I’s Of Big Data. This is a post about how the Big Data industry could do a much more effective job in promoting Big Data.

There was a time when talking about processor speeds, memory, and other specs really mattered in the computer industry. Having built my own simulated microprocessor back in the day, I still find that stuff interesting. But most people don’t.

Post Your Comment

Post Your Reply

Forbes writers have the ability to call out member comments they find particularly interesting. Called-out comments are highlighted across the Forbes network. You'll be notified if your comment is called out.